Automation and Applied Informatics Department, Politehnica University of Timisoara, Bvd. V. Parvan, No. 2, 300223 Timisoara, Romania.
Faculty of Automation and Computers, Politehnica University of Timisoara, Bvd. V. Parvan, No. 2, 300223 Timisoara, Romania.
Sensors (Basel). 2020 Sep 27;20(19):5539. doi: 10.3390/s20195539.
Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi-Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.
如今，智能交通理念已成为最重要的研究领域之一。我们所有人都依赖于机动性，即使在谈论人员、提供服务或运输货物时也是如此。研究人员试图创建和测试不同的交通模型，通过道路网络优化交通流量，并间接地减少旅行时间。为了验证这些新模型，出现了定义校准过程的必要性。在建模过程中，校准是强制性的，因为它确保了模型更接近实际系统。本文的目的是提出一种新的多学科方法，将微观交通建模理论与智能控制系统概念相结合，如模糊推理在交通模型校准中的应用。选择的 Takagi-Sugeno 模糊推理系统证明了其在实时系统中的自适应能力。这一概念将与卡尔曼滤波器结合，应用于特定的微观跟驰模型参数中。结果将证明微观交通模型参数如何根据实际数据进行自适应调整，从而证明模型的有效性。
